Quick model-based viscoelastic clot strength predictions from blood protein concentrations for cybermedical coagulation control

Cybermedical systems that regulate patient clotting in real time with personalized blood product delivery will improve treatment outcomes. These systems will harness popular viscoelastic assays of clot strength such as thromboelastography (TEG), which help evaluate coagulation status in numerous conditions: major surgery (e.g., heart, vascular, hip fracture, and trauma); liver cirrhosis and transplants; COVID-19; ICU stays; sepsis; obstetrics; diabetes; and coagulopathies like hemophilia. But these measurements are time-consuming, and thus impractical for urgent care and automated coagulation control. Because protein concentrations in a blood sample can be measured in about five minutes, we develop personalized, phenomenological, quick, control-oriented models that predict TEG curve outputs from input blood protein concentrations, to facilitate treatment decisions based on TEG curves. Here, we accurately predict, experimentally validate, and mechanistically justify curves and parameters for common TEG assays (Functional Fibrinogen, Citrated Native, Platelet Mapping, and Rapid TEG), and verify results with trauma patient clotting data.

We thank Dr. Mackle for her efforts on behalf of our manuscript, and we are very grateful to the reviewers for their positive evaluation of our work.In this response to reviewers, the reviewer comments are in black, our responses and descriptions of manuscript changes are in blue, and our highlights of new manuscript text are in red.We have made a few small manuscript edits in response to reviewer comments to appropriately revise our previous submission.

Associate Editor
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We thank Dr. Mackle for obtaining reviews of our manuscript.Our revised submission addresses all of the editorial requests as well as the points that were raised by the reviewers.We also include our code and data in a single zip file for reviewer assessment.Per Reviewer 2's request, this code and data has been uploaded to a GitHub repository (https://github.com/SYBORGS-Lab/Viscoelastic-Clot-Model)that is currently set to private.This repository will be made public when our manuscript is published.We greatly appreciate the opportunity to submit a revised manuscript for another round of review.

Reviewer 1
Thank you for the opportunity to review your manuscript.This is an elegant piece of work that builds nicely on the previous body of work by this group.This investigation tackles a very important real world clinical problem; the delay in obtaining the wealth of information available in TEG data.Going forward I would love to see the authors use this model to build on the work of Neilsen (reference #37) in more definitively characterizing the individual role of the all the coagulation factors roles in clot strength and dissolution.
We are very grateful to the reviewer for their time spent evaluating our work, and for recognizing its merit.We enthusiastically agree with the reviewer about the immediate next step that this work suggests.We have thus added text that describes this next step in the Discussion section, as follows.
In parallel, our models can be refined by incorporating the effects of adding individual coagulation factor concentrations into samples of normal and trauma patient plasma and whole blood, to confirm model-predicted clotting outcomes and theoretically ground previous literature observations 37 .Such work will also provide insight into treatment feasibility and efficacy from any proposed additional individual or combinatorial protein concentrations.
I have one comment.On Page 21 Figure 8 you refer to the green, yellow, and red boxes in Figure 1 C; I believe you are referring to the boxes in Figure 1 B.You make a similar connection in the text (Page 23, line 386).In either case, It is confusing to the reader to use the same colors in Figure 1 B and 1C as it causes the reader to try to make a connection between clot composition and the process used to conduct the study.
We truly appreciate the reviewer pointing out the confusion that arises from our color choices.Accordingly, we have revised the color palette of Fig. 1b to orange, cyan, and purple, so that there are no common colors with Fig. 1c, which remains blue, green, yellow, and red.We also now explicitly state the colors of Fig. 1b

Reviewer 2
The authors developed a "systems" model to simulate clot formation and degradation following trauma.This is an important problem.The authors have significantly advanced the field with this study.However, I have a few suggestions to improve the manuscript and promote open access to the findings.
We are very grateful to the reviewer for scrutinizing our manuscript, and for providing constructive comments that have enhanced our work and its accessibility.

Reviewers' Comments:
Reviewer #1: Remarks to the Author: Thank you for your comprehensive revisions.
Reviewer #2: Remarks to the Author: I thank the authors for responding to my previous issues, questions, and concerns.I look forward to the community effort to convert this codebase to PYTHON (or even better Julia).

Response to Reviewers
Nature Communications Manuscript NCOMMS-23-00139A Quick Model-Based Viscoelastic Clot Strength Predictions from Blood Protein Concentrations for Cybermedical Coagulation Control Damon E. Ghetmiri, Alessia J. Venturi, Mitchell J. Cohen, and Amor A. Menezes We thank Dr. Mackle for her efforts on behalf of our manuscript, and we are very grateful to the reviewers for their positive evaluation of our work.In this response to reviewers, the reviewer comments are in black, our responses and descriptions of manuscript changes are in blue, and our highlights of new manuscript text are in red.We have made a few small manuscript edits in response to the received editorial requests to appropriately revise our previous submission.

Associate Editor
Your manuscript entitled "Quick Model-Based Viscoelastic Clot Strength Predictions from Blood Protein Concentrations for Cybermedical Coagulation Control" has now been seen again by our referees, whose comments appear below.In light of their advice I am delighted to say that we are happy, in principle, to publish a suitably revised version in Nature Communications under the open access CC BY license (Creative Commons Attribution 4.0 International License).
We therefore ask that you edit your manuscript to comply with our policies and formatting requirements and to maximise the accessibility and therefore the impact of your work.
Please see the attached document(s), listing a number of points that must be addressed.Failure to comply with our editorial requests will cause delays in accepting your manuscript.Please also see the Nature Communications formatting instructions for further information.
We thank Dr. Mackle for obtaining reviews of our manuscript.Our revised submission addresses all of the editorial requests as well as the points that were raised by the reviewers.

Reviewer 1
Thank you for your comprehensive revisions.
We are very grateful to the reviewer for their time spent evaluating our work, and for recognizing its merit.
Reviewer 2 I thank the authors for responding to my previous issues, questions, and concerns.I look forward to the community effort to convert this codebase to PYTHON (or even better Julia).
We are very grateful to the reviewer for scrutinizing our manuscript, and for providing constructive comments that have enhanced our work and its accessibility.We enthusiastically endorse community efforts to translate our code into Python, Julia, and/or C.
in the Fig.1caption, just as we had previously done with Fig.1c, to clarify a color difference between panels.